An important aspect of nursing is the nursing process, a problem-solving approach applied to patient care. This nursing process includes four basic steps, which are assessment, planning, implementation and evaluation. Assessment involves the collection of signs and symptoms of a patient and the generation of prioritized nursing diagnoses by the nurse. Planning includes developing a plan of care to reach achievable outcomes for the patient based on the prioritized nursing diagnoses. The plan is then implemented for the patient, the outcomes are evaluated and the plan is revised.
Automation of the nursing process has been attempted in the past with concentration in the area of assessment and planning. Current automated systems, or expert systems, for generating nursing diagnoses fail to consider the method by which nurses actually make diagnoses. The diagnostic reasoning of nurses has been described by Carnevali, et al. in Diagnostic Reasoning in Nursing (New York: J. B. Lippincott, 1984) pages 25-28, 61-82, 193-206, which is hereby incorporated by reference. Carnevali et al. explain how nurses perform diagnoses based on assessment data, recognizing that there is some probabilistic relationship between the data and the diagnoses. However, the diagnostic reasoning process of nurses is also affected by a variety of biases which include the level of experience of the nurse, the amount of assessment data, stereotyping of patients, the frequency with which specific diagnoses and patient signs and symptoms occur, frequency of experience with specific signs and symptoms of the patient, and other experiences. Therefore, nurses may not always make correct diagnoses, and may fail to make important diagnoses. What Carnevali fails to note is that nurses often make multiple diagnoses which need to be prioritized in order to develop an adequate, prioritized care plan. The same biases which affected diagnoses also affect prioritization.
Another difficulty faced by nurses in performing diagnoses is that standard acceptable nursing diagnoses, generated by, for example, the North Atlantic Nursing Diagnoses Association (NANDA), are often insufficient to meet the needs of many specialized practice areas, and often change from year to year. Thus, nurses must continually keep up-to-date to know and use these nursing diagnoses. Current automated decision support systems, or expert systems, in nursing have failed to consider the constant change of acceptable nursing diagnoses. These previous systems include rules for making diagnoses which are "hard-coded" into the system. That is, when a system is implemented as a computer program in a computer language, such as LISP, the rules for diagnosis are also implemented in this computer language. Therefore, if any change needs to be made to the rules for diagnosis, a programmer needs to modify the system. Since acceptable nursing diagnoses change frequently, the maintenance of these systems becomes expensive, rendering them unsuitable for widespread use in hospitals. Moreover, nurses are not able to modify what they perceive to be the probabilistic relationship between signs and symptoms and diagnosis.
Finally, nurses often have experience in areas of specialty. Patients are grouped together according to these specialties into hospital care units, such as the intensive care unit, or the ambulatory care unit. In different care units, diagnoses have different likelihoods, different probabilistic relationships with patient data and different priorities. An inexperienced nurse often needs to rely on other experienced nurses to perform regular duties correctly.
In view of the problems and limitations of previous support systems for assisting nurses in performing diagnoses, it is an object of the present invention to provide a data processing system and method for automatically performing prioritized nursing diagnoses and which helps to reduce biases of a nurse.
Another object of the present invention is to provide a data processing system which permits nurses to transfer easily between specialized care units and to perform patient diagnoses while having little experience in a new care unit.
It is a further object of the present invention to provide a data processing system for automatically making nursing diagnoses from patient assessment data which recognizes the probabilistic relationship between patient data and diagnoses and prioritizes nursing diagnoses.